2 citations · 3 across the 7 of their papers we have counts for
4 papers · 1 filter
The Avengers: A Simple Recipe for Uniting Smaller Language Models to Challenge Proprietary Giants
Yiqun Zhang, Hao Li, Chenxu Wang +11
Proprietary giants are increasingly dominating the race for ever-larger language models. Can open-source, smaller models remain competitive across a broad range of tasks? In this p…
SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning
Chenyang Zhao, Xueying Jia, Vijay Viswanathan +2
Large language models (LLMs) hold the promise of solving diverse tasks when provided with appropriate natural language prompts. However, prompting often leads models to make predic…
Training Task Experts through Retrieval Based Distillation
Jiaxin Ge, Xueying Jia, Vijay Viswanathan +2
One of the most reliable ways to create deployable models for specialized tasks is to obtain an adequate amount of high-quality task-specific data. However, for specialized tasks,…
GenTKG: Generative Forecasting on Temporal Knowledge Graph with Large Language Models
Ruotong Liao, Xu Jia, Yangzhe Li +2
The rapid advancements in large language models (LLMs) have ignited interest in the temporal knowledge graph (tKG) domain, where conventional embedding-based and rule-based methods…